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Solutions Architect Resume



Responsible for designing and implementing the infrastructure needed to store, process and analyze huge data sets by collaborating with cross - functional teams and customer.


Big Data Platform: Hadoop, Confidential, Cloudera, Hortonworks

Cloud Implementation: AWS, Azure, Oracle(OCI), Cloud Formation, Terraform

Micro-services: Docker, Kubernetes, Mesosphere

Data Ingestion/ETL: Spark, Kafka, Pig, Hive, Elastic Search, Flume, Talend

NoSQL/ MPP DB: Cassandra, HBase, MongoDB, Presto, EnterpriseDB

Scripting Tools: R-Studio, Python, Scala, Perl, Zeppelin

BI Tools: Tableau, Kibana, Pentaho


Solutions Architect

Confidential, Dallas


  • Performing hands-on architecture and solution design of analytics applications used by customers and their internal users at scale on Cloud Infrastructure.
  • Building solutions that use Apache Kafka alongside Hadoop, relational and NoSQL databases, message queues, and related products as MPP Database, Scala and Python.
  • Working closely with Management and Decision Science team in the identification, acquisition, cleansing and visualization of data to support various data analytical projects.
  • Preparing and presenting potential technical solutions, advice business, and product owners on the technical value of proposals, including tradeoffs and opportunities.
  • Building next generation data services platform using Microservice, Docker Container, Kubernetes and Cloud native services.

Data Architect

Confidential, Dallas


  • Developed data pipeline for advanced analytics, with processing frameworks like Hadoop, Apache Spark and Streaming technologies such as Kafka, Flume and Kinesis.
  • Build highly scalable and extensible Big Data platforms on AWS, which enabled collection and analysis of massive data sets including those from IoT and streaming data.
  • Managed Hadoop and Spark cluster environments, on bare-metal and container infrastructure, including configuration for the cluster and capacity planning.
  • Leading the tool selection and development of key projects implementing Big Data technologies with client's On-Premise data centers or using Cloud Infrastructure.

Hadoop Lead Engineer

Confidential, Chicago


  • Developed automation framework for installation of Hadoop ecosystem components like HBase, HDFS, Map/Reduce, Yarn, Oozie, Pig, Hive, Impala, Spark and Kafka.
  • Leveraging the ETL tools like Pentaho, Control-M and Subversion for developing efficient solutions for data management, conversion, migration and integration.
  • Migrated legacy POS application data from Mainframe to Hadoop distributed platform.
  • Applying statistical techniques such as Segmentation Analysis, Time Series Analysis to develop and monitor scorecards.
  • Worked with Data Science team to implement the machine learning algorithms for advanced analytics use cases including demand forecasting, classification and clustering.

Senior Data Engineer



  • Implemented Big Data ecosystem (Hadoop, Map Reduce, Sqoop HBase, Hive, Pig and Mongo DB) to derive insights/analytics from data.
  • Build applications for finding baseline stats with mean, deviation and implementing Social Media Analysis, Recommendation and Trends Prediction using R programming.
  • Collaborating with project teams (managers, architects, data science) on tools and technology related to the design and development of Big Data solutions in Agile.

Data Engineer



  • Developed Entity-Relationship models for Retail, Insurance, and Life Science customer. Specific work in modeling data warehouse and operational data store.
  • Experience with advanced level of SQL programming and performance tuning techniques for Data Integration and Consumption within an OLTP, OLAP, and MPP architecture.
  • Build ETL mappings for processing fact, dimension tables with complex transformation.
  • Developed key modules Mappings, Workflows using Informatica, Reports using OBIEE.

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